There is growing agreement that
wireless capacity (at the PHY and MAC layers) is reaching saturation.
Many believe that the
next “jump” in network capacity will emerge from new ways
of organizing networks. While there exists substantial work on new
network architectures, one assumption that most proposals
seem to make is that infrastructure – WiFi APs, enterprise WLANs,
cell towers – is static. This project considers the possibility of
relaxing this assumption and explores the implications of physically
moving wireless network infrastructure to improve/optimize desired
performance metrics. For example, we envision WiFi access points on
wheels that move within a small region to exploit the multipath nature
of wireless signals; in the future, we envision drones flying into high
demand areas, hovering at strategic locations, and serving as cellular
proxies to ground clients. This project is a foray into the landscape
of such "robotic wireless networks".

Figure above shows an iMob AP
assembled using a Roomba iRobot
2.1, a webcam, and a laptop equipped with Intel 5300 802.11n
cards. The laptop is mounted on the iRobot and connected to it
over the serial interface; it is also connected to a Microsoft live
cam (attached in front of the iRobot) to guide its motion. The
laptop acts as the controller for the whole system, sending motion
commands to the robot (via the OSI interface), while also
controlling the network interface for transmission/reception. 8
laptop clients were uniformly scattered at various locations and
programmed to communicate back to the iMob AP.
The robot’s mobility is confined within a 2x2 feet square region,
demarcated by colored duct tapes pasted on the floor. If the
robot drifts out of the square box, the camera detects the color
of the duct tapes and triggers a change in heading direction.
The AP
performs “raster scans” within the square box at a
speed of 10 cm/sec – during the scan, the AP continuously sends
around 200 packets/second, equivalent to 60 packets per 3cms.
Transmissions are performed on regular OFDM with 3x3 MIMO
at both 2.4GHz and 5GHz bands. Clients record the per-packet
channel state information (CSI) for offline analysis.

Future
Directions: Using drones as a cellular proxy. The key research
challenge pertains to computing the
location
at
which the drone should hover so that it maximizes, for
example, the sume of
SNR
to
all the clients that must connect to the drone. Searching for
this location through
through
an
efficient algorithm is non-trivial, however, ray tracing
simulations may be
used
to
guide the motion path of the drone. The problem bears
similarity to active
learning.

UIUC/USC
Collaboration:

Periodic brainstorming sessions and discussions with PI
Nelakuditi and his team from USC. The discussions are mainly focussed
on characterizing the practical aspects of Robotic Networks, namely the
ramifications of imprecise motion of the robot, the limits of velocity,
the difficulty in instantaneous braking, etc. These discussions have
led to ironing out various pragmatic parts of the paper -- the findings
and experiences from these exercises are being prepared for a
submission to the IEEE Transactions of Mobile Computing. PI Roy
Choudhury and Nelakuditi also submitted a CRI proposal to NSF.

Rufeng Meng, one of PI Nelakuditi's PhD students,
visited UIUC from Summer 2015 to Fall 2015. He was collaborating on
various aspects of mobility algorithms.

Ongoing collaboration with the USC team is focussing on
drones and how they could serve as proxies to cellular towers, i.e., a
drone flies into a region of high network congestion, positions itself
strategically, and offers WiFi connectivity with cellular backhaul to
the actual cell tower. Algorithmic questions pertain to the "search
algorithm" so that the drone could find the best "hovering location".